Mainstreaming Human Security in the New Security Landscape: the discursive struggle for "freedom from fear" and "freedom from want"

After the failure to respond adequately to the changing patterns of conflict and violence after the end of the Cold War, human security was popularized in the 1990s as an alternative concept to security more suitable to face threats to individual security. Ever since, the question about what human security should entail has seen much discussion. This paper examines the different discourses which influence human security as ”freedom from fear” and ”freedom from want”. It situates the concept in what can be understood as the new security landscape, and examines how the different understandings of human security have influenced policy at the level of the United Nations. Performing an analysis based in the theoretical foundation of Ernesto Laclau and Chantal Mouffe’s discourse theory, this paper presents how human security is the target of a discursive struggle, as different discourses seek to hegemonize the concept. The paper goes on to analyze at a discursive level the hegemonization of human security within the UN, and concludes by presenting several reasons to why human security has increasingly been seen as a preventive approach

Monitoring the wetland landscape: white-faced ibis (Plegadis chihi) breeding habitat as a model assemblage

Wetlands structure landscape biodiversity by providing critical habitat to numerous fish and wildlife species. However, climate change, growing human populations, and shifting land use practices strain limited water supplies that sustain wetlands in the semi-arid western US. Conserving a wetland network with prominent value to wildlife is paramount to ensure future security of habitat and ecosystem processes. Here, I use white-faced ibis (Plegadis chihi; hereafter ‘ibis’) breeding colonies as a model system to identify and monitor a landscape-scale wetland network across the semi-arid western US. Ibis serve an important role in marking ecologically important wetland networks because they require a wide range of wetland habitats near colony locations for nesting and foraging. My analysis encompasses 153 breeding colonies in eight regions, derived from ecoregions, located on private and public lands. I evaluate long- term (1988-2020) patterns of wetland availability at ibis breeding colonies using surface water as a proxy for wetland flooding. Surface water trends are examined based on individual colony, region, ownership, hydrology (i.e. annual duration of wetland flooding), and wetland types (e.g. flood-irrigated agriculture, managed wetlands). To identify landscape drivers influencing flooding patterns, I link long-term trends to regional climate and anthropogenic factors. Analysis shows that approximately 60% of individual colony locations experienced wetland drying, and 5 of the 8 regions showed significant declines in wetland availability. Snow-water equivalent, daily minimum temperature, and irrigation were prevalent drivers of wetland trends. Publicly managed wildlife refuges, a central component to the colony network, were specifically impacted by patterns of wetland drying. These areas provide important over-water nesting locations in semi- permanent wetlands. Additionally, declines in flood-irrigated agriculture impacted adjacent ibis colonies through reduced foraging habitat. While underlying mechanisms influencing individual wetland sites are complex, pervasive drying of nesting and foraging habitat imperils the wetland network resiliency. Regional coordination and private-public partnerships are key to the long- term viability of a wetland network that benefits ibis and numerous other wetland-dependent species

Towards a Security, Privacy, Dependability, Interoperability Framework for the Internet of Things

A popular application of ambient intelligence systems constitutes of assisting living services on smart buildings. As intelligence is imported in embedded equipment, the system becomes able to provide smart services (e.g. control lights, airconditioning, provide energy management services etc.). IoT is the main enabler of such environments. However, the interconnection of these cyber-physical systems and the processing of personal data raise serious security and privacy issues. In this paper we present a framework that can guarantee Security, Privacy, Dependability and Interoperability (SPDI) in IoT. Taking advantage of the underlying IoT deployment, the proposed framework not only implements the requested smart functionality but also provide modelling and administration that can guarantee those SPDI properties. Moreover, we provide an application example of the framework in a smart building scenario

Issues Affecting Security Design Pattern Engineering

Security Design Patterns present the tried and tested design decisions made by security engineers within a well documented format. Patterns allow for complex security concepts, and mechanisms, to be expressed such that non domain experts can make use of them. Our research is concerned with the development of pattern languages for advanced crypto-systems. From our experience developing pattern languages we have encountered several recurring issues within security design pattern engineering. These issues, if not addressed, will affect the adoption of security design patterns. This paper describes these issues and discusses how they could be addressed

Predictive Cyber Situational Awareness and Personalized Blacklisting: A Sequential Rule Mining Approach

Cybersecurity adopts data mining for its ability to extract concealed and indistinct patterns in the data, such as for the needs of alert correlation. Inferring common attack patterns and rules from the alerts helps in understanding the threat landscape for the defenders and allows for the realization of cyber situational awareness, including the projection of ongoing attacks. In this paper, we explore the use of data mining, namely sequential rule mining, in the analysis of intrusion detection alerts. We employed a dataset of 12 million alerts from 34 intrusion detection systems in 3 organizations gathered in an alert sharing platform, and processed it using our analytical framework. We execute the mining of sequential rules that we use to predict security events, which we utilize to create a predictive blacklist. Thus, the recipients of the data from the sharing platform will receive only a small number of alerts of events that are likely to occur instead of a large number of alerts of past events. The predictive blacklist has the size of only 3 % of the raw data, and more than 60 % of its entries are shown to be successful in performing accurate predictions in operational, real-world settings